1. Field of the Invention
The invention is related to video processing. More particularly, the invention is related to detection and segmentation of a learned class of objects in video data.
2. Description of the Related Art
Recent developments in digital imagery, digital video and the increase in capacity of data storage have produced many types of automatic object recognition and object identification. Improvements in the precision of digital cameras and other image capture systems have provided unprecedented amounts of data to by analyzed and used by various analysis methods. Improvements in processing speeds have allowed for increased performance and more detailed analysis, but efficient analysis is still beneficial in terms of time and power savings.
Image segmentation involves partitioning a digital image into multiple regions (groups of pixels). One of the goals of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Typically, each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are different with respect to similar characteristics. Image segmentation can be performed at very fine scales due to the larger pixel densities afforded by modern image capture systems. However, very fine scale segmentation drives up the power consumption necessary to perform segmentation. Image segmentation at larger scales can improve the efficiency of the segmentation process, but accuracy can be degraded.
Given image segments for a known class of objects (e.g., a set of pre-segments object images), a classifier model can be learned from the known images to automatically categorize the objects in future images. Simple classifiers built based on some image feature of the object tend to be weak in categorization performance. Using boosting methods for object categorization is a way to unify the weak classifiers in a special way to boost the overall ability of categorization. Again, due to the fine detail available in image capture equipment, object classification can be performed at a very fine scale given adequate processing power and time. Object classification can also be performed at larger scales to speed up the process at the expense of accuracy.